seaborn assignment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tc=t.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aaade430c8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(tc,annot=False,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aaa9e4f248>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sex',data=t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Laptop\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1324: RuntimeWarning: invalid value encountered in less\n",
      "  off_low = points < low_gutter\n",
      "C:\\Users\\Laptop\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1328: RuntimeWarning: invalid value encountered in greater\n",
      "  off_high = points > high_gutter\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aaa9ea6ec8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x='pclass',y='age',data=t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aaaa609088>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='age',x='pclass',data=t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aaadedf388>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BDmBzW9/M4Nz0TP3q9sn7RuC5mb8CLgcZHKLfDOypqo8O7eq134l2xE6SlwJvZvBh413AlW3Ygf3O/He4Eriz2gnaY11V3VBVa6pqHYM/l3dW1bvosFeAJC9L8oqZdeAS4EGW+rW81B9ELPBDjMuBf2Vw7vJPlno+Y+jnc8CTwP8xeHffwuDc4y7gkbY8rY0Ng6uFvg88AGxY6vkfYa+/xuCvovcD97XH5R33+1rg3tbvg8CftvqrgG8Ce4F/AE5q9ZPb9t62/1VL3cM8+34jcEfPvba+vtMeD81k0VK/lr39gCR1aDmflpEkHYLhLkkdMtwlqUOGuyR1yHCXpA4Z7jquJfmDdmfKzy71XKRx8lJIHdfa18Qvq6p/G2HsivrFvVGkY9pyvreMtCBJ/pbBF1B2JPl7YBPwUuB/gGuq6uEk7wbewuCLNi8DLkryh8BvAycBt1XVB5Zi/tLhGO46blXV7yW5FHgT8L/AX9XgH3h/M/AXwG+1oW8AXltVTye5hMF9uM9n8E3DHUl+vaq+vgQtSIdkuEsDpwDbk6xncFuEE4f27ayqmRtZXdIe97btlzMIe8NdxxTDXRr4M+CuqvrNdn/5u4f2/ffQeoAPVdUnjt7UpCPn1TLSwCnAf7T1dx9m3FeB3233oSfJ6nYPb+mYYrhLA38JfCjJPwMnHGpQVX0NuAX4lyQPAF8EXnF0piiNzkshJalDHrlLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktSh/webcZalC7SAnwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(t['fare'],kde=False,bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1aaadd78fc8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='fare',y='age',data = t,kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data = t,col='sex') #important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Axes instance argument was not found in a figure",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-6e99a29187f6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'age'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mmap\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m    728\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    729\u001b[0m             \u001b[1;31m# Get the current axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 730\u001b[1;33m             \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfacet_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow_i\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol_j\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    731\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    732\u001b[0m             \u001b[1;31m# Decide what color to plot with\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mfacet_axis\u001b[1;34m(self, row_i, col_j)\u001b[0m\n\u001b[0;32m    858\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    859\u001b[0m         \u001b[1;31m# Get a reference to the axes object we want, and make it active\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 860\u001b[1;33m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    861\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    862\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36msca\u001b[1;34m(ax)\u001b[0m\n\u001b[0;32m    903\u001b[0m             \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    904\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 905\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Axes instance argument was not found in a figure\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    906\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    907\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Axes instance argument was not found in a figure"
     ]
    }
   ],
   "source": [
    "g.map(sns.distplot,'age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}